datasheet1_Prediction of Function Determining and Buried Residues Through Analysis of Saturation Mutagenesis Datasets.xlsx

Mutational scanning can be used to probe effects of large numbers of point mutations on protein function. Positions affected by mutation are primarily at either buried or at exposed residues directly involved in function, hereafter designated as active-site residues. In the absence of prior structural information, it has not been easy to distinguish between these two categories of residues. We curated and analyzed a set of twelve published deep mutational scanning datasets. The analysis revealed differential patterns of mutational sensitivity and substitution preferences at buried and exposed positions. Prediction of buried-sites solely from the mutational sensitivity data was facilitated by incorporating predicted sequence-based accessibility values. For active-site residues we observed mean sensitivity, specificity and accuracy of 61, 90 and 88% respectively. For buried residues the corresponding figures were 59, 90 and 84% while for exposed non active-site residues these were 98, 44 and 82% respectively. We also identified positions which did not follow these general trends and might require further experimental re-validation. This analysis highlights the ability of deep mutational scans to provide important structural and functional insights, even in the absence of three-dimensional structures determined using conventional structure determination techniques, and also discuss some limitations of the methodology.

突变扫描技术被广泛应用于探究大量点突变对蛋白质功能的影响。受突变影响的位点主要位于埋藏或暴露的残基，这些残基直接参与功能过程，以下将此类残基命名为活性位点残基。在缺乏先验结构信息的情况下，区分这两类残基一直颇具挑战。本研究收集并分析了十二组公开发表的深度突变扫描数据集。分析揭示了埋藏和暴露位点在突变敏感性和取代偏好方面的差异模式。通过整合基于序列预测的可达性值，仅从突变敏感性数据预测埋藏位点变得更加容易。对于活性位点残基，我们观察到平均敏感性、特异性和准确率分别为61%、90%和88%。对于埋藏残基，相应的数据为59%、90%和84%，而对于暴露的非活性位点残基，这些数据分别为98%、44%和82%。我们还识别出了一些不符合这些一般趋势的位点，这些位点可能需要进一步的实验验证。该分析凸显了深度突变扫描在提供重要的结构和功能见解方面的能力，即使在缺乏使用传统结构确定技术确定的蛋白质三维结构的情况下，同时也讨论了该方法的某些局限性。